AI Explainability Audit & Assessment
We review your organization's AI systems to identify where transparency gaps exist and map out how your algorithms actually make decisions. Our assessment covers model documentation, decision pathways, and potential bias concerns. You'll receive a detailed roadmap showing what regulators expect and where your current practices need adjustment.
Model documentation
Decision transparency
Bias assessment
Regulatory alignment
Compliance Documentation Framework
Structured guidance for documenting your AI systems in language that satisfies both auditors and regulators. We help you create and maintain documentation that clearly explains algorithmic logic, decision rules, and governance structures. This framework becomes your reference point for ongoing compliance conversations with oversight bodies.
Documentation standards
Decision logic mapping
Model cards
Audit trails
Algorithm Fairness Testing & Validation
Testing your algorithms against regulatory fairness requirements and real-world bias scenarios. We examine how your models perform across different customer segments and demographics to identify disparate impact concerns. This includes feature importance analysis, threshold validation, and recommendations for model adjustments if needed.
Fairness testing
Disparate impact analysis
Feature validation
Model threshold review
AI Governance Workshop & Training
Half-day or full-day workshops tailored for your compliance, risk, and technology teams. We walk through regulatory expectations, show how to explain algorithmic decisions in audit meetings, and help teams understand what documentation auditors will actually request. Practical, focused on real scenarios your organization faces.
Regulatory landscape
Audit readiness
Cross-team alignment
Governance setup
